NEAT—Named Entities in Archaeological Texts: A semantic approach to term extraction and classification

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3A2UBM235M" target="_blank" >RIV/00216208:11320/23:2UBM235M - isvavai.cz</a>

Výsledek na webu

<a href="https://academic.oup.com/dsh/advance-article-abstract/doi/10.1093/llc/fqad017/7117781" target="_blank" >https://academic.oup.com/dsh/advance-article-abstract/doi/10.1093/llc/fqad017/7117781</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1093/llc/fqad017" target="_blank" >10.1093/llc/fqad017</a>

Jazyk výsledku

angličtina

Název v původním jazyce

NEAT—Named Entities in Archaeological Texts: A semantic approach to term extraction and classification

Popis výsledku v původním jazyce

"In this paper, we propose a methodology to annotate texts concerning domain-specific knowledge, to provide a reliable source of data for the task of Named Entity Recognition (NER) in the domain of archaeology for the Italian laguage. This method integrates syntactic and semantic information from several structured sources to annotate entities’ mentions in unstructured texts. Furthermore, we make use of an ontology to label entities with the specific type they refer to. By using a corpus made up of item descriptions from Europeana’s Archaeology Collection, we first test our proposed methodology on a mock dataset composed of 1,000 texts. After several steps of improvements, we use the final process to create a complete dataset composed of 5,000 descriptions. The resulting dataset, Named Entities in Archaeological Texts has a total of 41,002 spans of texts annotated with their domain-specific entity classification according to the CIDOC Conceptual Reference Model."

Název v anglickém jazyce

NEAT—Named Entities in Archaeological Texts: A semantic approach to term extraction and classification

Popis výsledku anglicky

"In this paper, we propose a methodology to annotate texts concerning domain-specific knowledge, to provide a reliable source of data for the task of Named Entity Recognition (NER) in the domain of archaeology for the Italian laguage. This method integrates syntactic and semantic information from several structured sources to annotate entities’ mentions in unstructured texts. Furthermore, we make use of an ontology to label entities with the specific type they refer to. By using a corpus made up of item descriptions from Europeana’s Archaeology Collection, we first test our proposed methodology on a mock dataset composed of 1,000 texts. After several steps of improvements, we use the final process to create a complete dataset composed of 5,000 descriptions. The resulting dataset, Named Entities in Archaeological Texts has a total of 41,002 spans of texts annotated with their domain-specific entity classification according to the CIDOC Conceptual Reference Model."

Druh

J_ost - Ostatní články v recenzovaných periodicích

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

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Návaznosti

—

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

"Digital Scholarship in the Humanities"

ISSN

2055-7671

e-ISSN

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Svazek periodika

38

Číslo periodika v rámci svazku

2023-5-24

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

17

Strana od-do

997-1013

Kód UT WoS článku

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EID výsledku v databázi Scopus

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